A look at the latest Jedlovec House eletricity usage and solar production from PPL and Enphase.
Load packages
library(tidyverse)
library(lubridate)
library(hms)
library(readxl)
library(onlineBcp)
Load PPL data
col_datatypes <- c('numeric','numeric','date','text',rep('numeric',99))
hourly1 <- read_excel("Hourly Usage 220326 to 220624.xlsx", col_types = col_datatypes)
Warning: Expecting numeric in A277 / R277C1: got 'The information contained in this file is intended for the confidential use by the customer and third parties authorized by the customer to receive the information. Any unauthorized use is prohibited.'
hourly2 <- read_excel("Hourly Usage 220625 to 230623.xlsx", col_types = col_datatypes)
Warning: Expecting numeric in A1096 / R1096C1: got 'The information contained in this file is intended for the confidential use by the customer and third parties authorized by the customer to receive the information. Any unauthorized use is prohibited.'
hourly3 <- read_excel("PPL 230624 to 240509.xlsx", col_types = col_datatypes)
Warning: Expecting numeric in A967 / R967C1: got 'The information contained in this file is intended for the confidential use by the customer and third parties authorized by the customer to receive the information. Any unauthorized use is prohibited.'
#hourly1
#hourly2
#hourly3
ppl_15mins <- bind_rows(hourly1,list(hourly2,hourly3))
ppl_15mins %>% arrange(desc(Date), `Read Type`)
NA
NA
Transform PPL Data
hourly_ppl_pivot <- ppl_15mins %>%
rename(date = Date) %>%
pivot_longer(!c("Account Number", "Meter Number", date, "Read Type", Min, Max, Total), names_to = "time", values_to = "kWh")
rename(ppl_15mins, date = Date)
hourly_ppl_pivot <- hourly_ppl_pivot %>%
mutate(time = parse_time(time, '%H:%M %p'), month = month(date, label=TRUE), year = year(date), yday = yday(date), wday = wday(date, label=TRUE))
(hourly_ppl_net <- hourly_ppl_pivot %>%
filter(`Read Type` == "kWh Net"))
Load Enphase data
import <- read_csv("enphase_history_230701.csv")
Rows: 42432 Columns: 2── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Date/Time
dbl (1): Energy Produced (Wh)
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
update <- read_csv("hourly_generation_230625_240510.csv")
Rows: 30816 Columns: 2── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Date/Time
dbl (1): Energy Produced (Wh)
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
update <- update %>%
mutate(`Date/Time` = as.POSIXct(`Date/Time`,format="%m/%d/%Y %H:%M",tz=Sys.timezone())) %>%
filter(`Date/Time` >= as.POSIXct('07/01/2023 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) )
import <- import %>%
mutate(`Date/Time` = as.POSIXct(`Date/Time`,format="%m/%d/%Y %H:%M",tz=Sys.timezone()))
full_hist <- rbind(import,update)
full_hist %>% arrange(desc(`Date/Time`))
(hourly_production <- full_hist %>%
rename(datetime = `Date/Time`, energy_produced_Wh = `Energy Produced (Wh)`) %>%
mutate(datetime = as.POSIXct(datetime,format="%m/%d/%Y %H:%M",tz=Sys.timezone())) %>%
mutate(date = date(datetime), time = as.hms(format(datetime, format = "%H:%M:%S")), month = month(datetime, label=TRUE), year = year(datetime), day = day(datetime), yday = yday(datetime), monthday = format(datetime, "%m-%d"), wday = wday(datetime, label=TRUE), equinox_day = (yday + 10) %% 365, equinox_group = floor((equinox_day+15)/30)*30)
)
NA
Spot-check Enphase Should see lifetime production by day and by hour
ggplot(hourly_production, aes(datetime, energy_produced_Wh)) +
geom_point()
ggplot(hourly_production, aes(time, energy_produced_Wh)) +
theme(axis.text.x = element_text(angle = 90)) +
geom_point()
Net + Produced = Consumed
# hourly_ppl_net <- hourly_ppl_net %>% mutate(date = as_date(date))
#hourly_ppl_net %>% arrange(desc(date))
#hourly_production %>% arrange(desc(date))
(hourly_electricity <- hourly_ppl_net %>%
inner_join(hourly_production, by = join_by(date,time)) %>%
mutate(consumed_kWh = kWh + energy_produced_Wh/1000, produced_kWh = energy_produced_Wh/1000) %>%
rename(net_kWh = kWh) %>%
select(datetime, date, time, net_kWh, produced_kWh, consumed_kWh) %>%
arrange(date))
NA
Calculate production for first year of solar panels, second year, etc.
Interesting! Produced kWh went down by 5%, but consumed kWh went down by 10% despite the fact that we got an electric car! Let’s explore that further.
daily_electricity <- hourly_electricity %>%
group_by(solar_year, yday) %>%
summarize(net_kWh = sum(net_kWh), produced_kWh = sum(produced_kWh), consumed_kWh = sum(consumed_kWh))
`summarise()` has grouped output by 'solar_year'. You can override using the `.groups` argument.
ggplot(daily_electricity, aes(yday, consumed_kWh, group=solar_year, color=solar_year)) +
geom_point() +
geom_smooth(span=0.3) +
scale_x_continuous(breaks = c(1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335), labels = month.abb) +
theme(axis.text.x = element_text(angle = 90))
Daily totals
daily_electricity <- hourly_electricity %>%
mutate(solar_day = interval(as_date(as.POSIXct('04/15/2022',format="%m/%d/%Y",tz=Sys.timezone())),as_date(date)) / days(1)
) %>%
group_by(date, solar_day) %>%
summarize(net_kWh = sum(net_kWh), produced_kWh = sum(produced_kWh), consumed_kWh = sum(consumed_kWh))
`summarise()` has grouped output by 'date'. You can override using the `.groups` argument.
ggplot(daily_electricity, aes(date, consumed_kWh)) +
geom_point() +
#scale_x_continuous(breaks = c(1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335), labels = month.abb) +
theme(axis.text.x = element_text(angle = 90))
NA
NA
Look how our electricity consumption got much less predictable, more uneven, after we purchased an EV and installed a level 2 charger in late January/early February 2023!
Also, a couple of outlier high points in Dec 2022 before we got the EV… hmm.
We probably need to filter out the EV charging and compare that separately.
Average consumption based on time of day
#hourly_electricity %>% summarize(min_date = min(date), max_date = max(date))
electricity_by_time <- hourly_electricity %>%
filter(solar_year == 1 | solar_year == 2) %>%
#filter(date <= as.POSIXct('12/31/2022 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ) %>%
group_by(time, solar_year) %>%
summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>%
arrange(time)
`summarise()` has grouped output by 'time'. You can override using the `.groups` argument.
electricity_by_time
ggplot(electricity_by_time, aes(x=time, y=consumed_kWh, group=solar_year, color=solar_year)) +
geom_point()
#labs(colour="",x="Time of Day",y="Electricity Consumption (kWh)")+
#scale_color_manual(values = c("red","black","green")) +
#ggtitle("Home Electricity in 15-Minute Intervals (Since April 15, 2022)")
Wow, look how much less was used during the day in year 2!!! Like a 50% reduction!
Is this due to milder weather?
Let’s look at it by month:
#electricity_by_month <-
hourly_electricity %>%
mutate(month = month(date)) %>%
group_by(solar_year, month) %>%
summarize(consumed_kWh = mean(consumed_kWh)) %>%
arrange(month, solar_year) %>%
pivot_wider(names_from = month, values_from = consumed_kWh) %>%
arrange(solar_year)
`summarise()` has grouped output by 'solar_year'. You can override using the `.groups` argument.
#Look at limited window during day (no EV charging)
# hourly_electricity %>%
# filter(time == '12:00:00') %>%
# #filter(time > as.POSIXct('04:00:00',format="%H:%M:$s",tz=Sys.timezone()) ) %>% #& time < as.POSIXct('18:00',format="%H:%M",tz=Sys.timezone())) %>%
# mutate(month = month(date)) %>%
# group_by(solar_year, month) %>%
# summarize(consumed_kWh = mean(consumed_kWh)) %>%
# arrange(month, solar_year) %>%
# pivot_wider(names_from = month, values_from = consumed_kWh) %>%
# arrange(solar_year)
Look at specific months
electricity_by_month_time <- hourly_electricity %>%
mutate(month = as_factor(month(date))) %>%
filter(solar_year == 1 | solar_year == 2) %>%
#Sample month
filter(month == 6) %>%
group_by(solar_year, month, time) %>%
summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>%
arrange(solar_year, month, time)
`summarise()` has grouped output by 'solar_year', 'month'. You can override using the `.groups` argument.
ggplot(electricity_by_month_time, aes(time, consumed_kWh, group=solar_year, color=solar_year)) +
#facet_grid(rows = vars(month)) +
geom_point(size=0.5) +
ggtitle("Home Electricity in 15-Minute Intervals") +
ylab("Electricity Consumption (kWh)")
Example day with EV charging
sample_day <- hourly_electricity %>%
filter(solar_year == 1 | solar_year == 2) %>%
filter(yday == 161) %>%
group_by(date, solar_year, yday, time) %>%
summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>%
arrange(date, solar_year, yday, time)
`summarise()` has grouped output by 'date', 'solar_year', 'yday'. You can override using the `.groups` argument.
ggplot(sample_day, aes(time, consumed_kWh, group=solar_year, color=solar_year)) +
#facet_grid(rows = vars(month)) +
geom_point(size=0.5) +
ggtitle("Home Electricity in 15-Minute Intervals") +
ylab("Electricity Consumption (kWh)")
Ok, let’s detect and remove EV charging sessions.
Bayesian change point example
# library(onlineBcp)
x <- c(rnorm(10, 2.6, 0.2), rnorm(1, 2.6/2, 0.2) + rnorm(1, .2, .15), rnorm(86, .2, .15))
bcp <- online_cp(x)
summary(bcp)
[1] "Change points"
[1] "Segments"
begin end mean SD LL of CI UL of CI
[1,] 1 11 2.4159210 0.2461319 2.2938538 2.537988
[2,] 12 97 0.2128337 0.1483191 0.1865265 0.239141
bcp
$x
[1] 2.3720905715 2.4691440621 2.6071250448 2.5352472423 2.2877005354 2.3857597432 2.7419924694 2.3295656691
[9] 2.6664633812 2.3582885866 1.8217537652 0.0930585696 0.3230388724 0.0197739677 0.2570762359 0.1881634498
[17] 0.0792020254 0.1498961292 0.3642266363 0.3817626425 0.1885297288 0.3544041698 0.2879920055 0.0953605082
[25] 0.0446978082 0.0004123957 0.1959692210 0.0693755586 0.4736216346 0.1488568573 0.2188121995 0.0812417378
[33] 0.3080128352 0.4839328145 0.3567281316 0.0095019304 0.4530073963 0.3672416208 0.3294527060 0.1982238893
[41] 0.2996424154 0.1570348120 0.2920901580 0.2199320912 0.0822511010 0.4977626932 0.2455526621 0.4334779288
[49] 0.2376445401 0.2338382842 -0.0402372143 0.2225591660 0.1183037401 0.3194898662 0.1986949765 0.1917810490
[57] 0.0807082047 0.2332076344 -0.1524857206 0.0582340793 0.0665107477 0.0714005187 -0.0557819132 0.2851434290
[65] 0.3313943646 0.2103237245 -0.1036259056 0.1887882159 0.2929783093 0.0780226903 -0.1688743454 0.4017997097
[73] 0.3595800053 0.1888407383 0.1824374153 0.1784951176 0.1609317339 0.2098454959 0.3643482946 0.3487322792
[81] 0.2841377275 0.2780164560 0.0203063376 0.0949771645 0.2602946798 0.3359891574 0.1761905070 0.5173409818
[89] 0.1462872854 0.4356854056 0.1592010947 0.3728770785 0.2836787830 0.1263039884 0.4396970634 0.0967142059
[97] 0.2336566264
$max_p
[,1] [,2] [,3]
[1,] 0.9577733 0 0
[2,] 0.9383008 0 1
[3,] 0.9289778 0 2
[4,] 0.9231639 0 3
[5,] 0.9241976 0 4
[6,] 0.9287806 0 5
[7,] 0.9299975 0 6
[8,] 0.9358318 0 7
[9,] 0.9381648 0 8
[10,] 0.9254240 0 9
[11,] 0.4559258 0 10
[12,] 0.6111704 11 0
[13,] 0.8090391 11 1
[14,] 0.7996688 11 2
[15,] 0.7973283 11 3
[16,] 0.7991989 11 4
[17,] 0.8056386 11 5
[18,] 0.8089248 11 6
[19,] 0.8134703 11 7
[20,] 0.8252870 11 8
[21,] 0.8317779 11 9
[22,] 0.8411875 11 10
[23,] 0.8487528 11 11
[24,] 0.8538773 11 12
[25,] 0.8569608 11 13
[26,] 0.8680025 11 14
[27,] 0.8731902 11 15
[28,] 0.8663527 11 16
[29,] 0.8785399 11 17
[30,] 0.8858196 11 18
[31,] 0.8890118 11 19
[32,] 0.8923711 11 20
[33,] 0.8820230 11 21
[34,] 0.8895051 11 22
[35,] 0.8903923 11 23
[36,] 0.8879874 11 24
[37,] 0.8942148 11 25
[38,] 0.8993633 11 26
[39,] 0.9044254 11 27
[40,] 0.9066541 11 28
[41,] 0.9088015 11 29
[42,] 0.9107461 11 30
[43,] 0.9130828 11 31
[44,] 0.9113711 11 32
[45,] 0.9002217 11 33
[46,] 0.9116611 11 34
[47,] 0.9077451 11 35
[48,] 0.9147518 11 36
[49,] 0.9175931 11 37
[50,] 0.9030689 11 38
[51,] 0.9158742 11 39
[52,] 0.9176531 11 40
[53,] 0.9198118 11 41
[54,] 0.9222592 11 42
[55,] 0.9235917 11 43
[56,] 0.9208759 11 44
[57,] 0.9248626 11 45
[58,] 0.8835135 11 46
[59,] 0.9079202 11 47
[60,] 0.9164723 11 48
[61,] 0.9203609 11 49
[62,] 0.9085255 11 50
[63,] 0.9224407 11 51
[64,] 0.9244629 11 52
[65,] 0.9285093 11 53
[66,] 0.9042838 11 54
[67,] 0.9240923 11 55
[68,] 0.9277552 11 56
[69,] 0.9275764 11 57
[70,] 0.8848931 11 58
[71,] 0.9081096 11 59
[72,] 0.9189911 11 60
[73,] 0.9283150 11 61
[74,] 0.9312630 11 62
[75,] 0.9325588 11 63
[76,] 0.9331760 11 64
[77,] 0.9340624 11 65
[78,] 0.9292055 11 66
[79,] 0.9290267 11 67
[80,] 0.9322733 11 68
[81,] 0.9335335 11 69
[82,] 0.9274462 11 70
[83,] 0.9314169 11 71
[84,] 0.9346385 11 72
[85,] 0.9326805 11 73
[86,] 0.9357910 11 74
[87,] 0.9075830 11 75
[88,] 0.9290786 11 76
[89,] 0.9214965 11 77
[90,] 0.9325243 11 78
[91,] 0.9300501 11 79
[92,] 0.9343632 11 80
[93,] 0.9350886 11 81
[94,] 0.9243810 11 82
[95,] 0.9313401 11 83
[96,] 0.9362280 11 84
[97,] 0.0000000 0 0
$parameters
theta alpha beta th_cp
0.9 1.0 1.0 0.5
$series_length
[1] 97
$result
NULL
attr(,"class")
[1] "BayesCP"
Test on electricity consumption data
# library(onlineBcp)
#Select a sample to test
sample <- hourly_electricity %>%
mutate(solar_day = interval(as_date(as.POSIXct('04/15/2022',format="%m/%d/%Y",tz=Sys.timezone())),as_date(date)) / days(1) ) %>%
filter(solar_year == 2) %>%
filter(yday < 38 & yday > 35)
ggplot(sample, aes(datetime, consumed_kWh, group=solar_year, color=solar_year)) +
#facet_grid(rows = vars(month)) +
geom_point(size=0.5) +
ggtitle("Home Electricity in 15-Minute Intervals") +
ylab("Electricity Consumption (kWh)")
bcp model comparisons: https://lindeloev.github.io/mcp/articles/packages.html
min_time <- pull(sample %>% summarize(min_time = min(datetime)))
sample <- sample %>% mutate(time_x = interval(min_time,datetime)/minutes(15)+1)
#Filter data to feed to model
x <- sample %>%
ungroup() %>%
select(consumed_kWh)
x <- x$consumed_kWh
#Run online Bayesian Change Point model
#"Online" means in progress, not retroactive
bcp <- online_cp(x)
plot(summary(bcp))
[1] "Change points"
[1] "Segments"
begin end mean SD LL of CI UL of CI
[1,] 1 22 0.3472727 0.1636542 0.2898818 0.4046636
[2,] 23 71 0.3963061 0.6793674 0.2366690 0.5559433
[3,] 72 86 2.6233333 0.2818983 2.5036113 2.7430554
[4,] 87 118 0.3384375 0.1960597 0.2814289 0.3954461
[5,] 119 131 1.6671538 0.6909474 1.3519434 1.9823643
[6,] 132 192 0.4151148 0.2525212 0.3619333 0.4682962
Not too bad! It picks up the EV charging sessions, but it also picks up some other, presumably HVAC-related consumption changes. It looks like I should be able to weed these out easily.
#Select a sample to test
sample <- hourly_electricity %>%
#filter(solar_year == 2) %>%
mutate(solar_day = interval(as_date(as.POSIXct('04/15/2022',format="%m/%d/%Y",tz=Sys.timezone())),as_date(date)) / days(1) )
ggplot(sample, aes(datetime, consumed_kWh, group=solar_year, color=solar_year)) +
#facet_grid(rows = vars(month)) +
geom_point(size=0.5) +
ggtitle("Home Electricity in 15-Minute Intervals") +
ylab("Electricity Consumption (kWh)")
You can pick out our family vacation in August, and you can also see a period of high electricity usage in February, presumably related to cold weather. - Verify with weather data?
min_time <- pull(sample %>% summarize(min_time = min(datetime)))
sample <- sample %>%
mutate(time_x = interval(min_time,datetime)/minutes(15)+1)
#Filter data to feed to model
x <- sample %>%
ungroup() %>%
select(consumed_kWh)
x <- x$consumed_kWh
#Run online Bayesian Change Point model
#"Online" means in progress, not retroactive
bcp <- online_cp(x)
summary(bcp)
[1] "Change points"
[1] "Segments"
begin end mean SD LL of CI UL of CI
[1,] 1 68 0.11851471 0.05251481 0.10803969 0.12898972
[2,] 69 89 0.70085714 0.55165300 0.50284875 0.89886553
[3,] 90 233 0.14222917 0.09243684 0.12955874 0.15489959
[4,] 234 240 0.91714286 0.55476224 0.57224926 1.26203645
[5,] 241 408 0.37915476 0.27470555 0.34429375 0.41401578
[6,] 409 410 1.73000000 0.66468037 0.95691880 2.50308120
[7,] 411 524 0.45407895 0.32414913 0.40414226 0.50401563
[8,] 525 526 1.84250000 0.59609102 1.14919420 2.53580580
[9,] 527 599 0.17747945 0.06771880 0.16444253 0.19051638
[10,] 600 602 1.19966667 0.60550007 0.62464948 1.77468385
[11,] 603 696 0.22370213 0.15978499 0.19659401 0.25081024
[12,] 697 724 0.26989286 0.26216920 0.18839805 0.35138766
[13,] 725 792 0.21539706 0.23737175 0.16804904 0.26274508
[14,] 793 843 0.18211765 0.20866132 0.13405761 0.23017769
[15,] 844 888 0.14957778 0.17634812 0.10633717 0.19281839
[16,] 889 938 0.19734000 0.19177406 0.15273001 0.24194999
[17,] 939 943 0.86680000 0.05580502 0.82574978 0.90785022
[18,] 944 1018 0.17470667 0.10118618 0.15548824 0.19392509
[19,] 1019 1032 0.58428571 0.34496364 0.43263776 0.73593367
[20,] 1033 1142 0.18816364 0.08463429 0.17489039 0.20143689
[21,] 1143 1176 0.18558824 0.15582024 0.14163289 0.22954358
[22,] 1177 1178 1.59500000 0.60952605 0.88606809 2.30393191
[23,] 1179 1368 0.32744737 0.28211650 0.29378234 0.36111239
[24,] 1369 1375 0.95057143 0.51726327 0.62899080 1.27215205
[25,] 1376 1466 0.21749451 0.25720279 0.17314567 0.26184334
[26,] 1467 1560 0.21709574 0.22015376 0.17974584 0.25444564
[27,] 1561 1605 0.30462222 0.40168567 0.20612878 0.40311567
[28,] 1606 1714 0.17048624 0.14168952 0.14816328 0.19280920
[29,] 1715 1955 0.14351452 0.11244080 0.13160093 0.15542811
[30,] 1956 1959 0.84625000 0.16573950 0.70994139 0.98255861
[31,] 1960 2067 0.18087037 0.09166977 0.16636123 0.19537951
[32,] 2068 2144 0.19009091 0.11276725 0.16895286 0.21122896
[33,] 2145 2147 1.92800000 0.91683314 1.05732328 2.79867672
[34,] 2148 2232 0.44197647 0.43827136 0.36378473 0.52016821
[35,] 2233 2252 0.79275000 0.43621348 0.63231049 0.95318951
[36,] 2253 2328 0.15798684 0.11320336 0.13662790 0.17934579
[37,] 2329 2335 0.90457143 0.42456050 0.64062376 1.16851909
[38,] 2336 2472 0.20097810 0.18653806 0.17476403 0.22719217
[39,] 2473 2659 0.17288235 0.10114196 0.16071663 0.18504807
[40,] 2660 2670 0.43463636 0.27245266 0.29951564 0.56975709
[41,] 2671 2672 0.33850000 0.18031223 0.12878116 0.54821884
[42,] 2673 2676 0.37275000 0.11853375 0.27526467 0.47023533
[43,] 2677 3132 0.27140132 0.12122497 0.26206368 0.28073895
[44,] 3133 3414 0.24971277 0.10382509 0.23954314 0.25988239
[45,] 3415 3485 0.26271831 0.08814455 0.24551177 0.27992485
[46,] 3486 3548 0.78198413 0.43995383 0.69081158 0.87315667
[47,] 3549 3634 0.51816279 0.27488166 0.46940727 0.56691831
[48,] 3635 3718 0.33130952 0.13270603 0.30749301 0.35512604
[49,] 3719 3721 0.17733333 0.04707795 0.13262544 0.22204122
[50,] 3722 4070 0.15686819 0.10889972 0.14727990 0.16645649
[51,] 4071 4077 0.79457143 0.04656485 0.76562223 0.82352062
[52,] 4078 4292 0.15275349 0.10925063 0.14049796 0.16500902
[53,] 4293 4445 0.38272549 0.31320973 0.34107532 0.42437566
[54,] 4446 4544 0.58247475 0.29179862 0.53423635 0.63071315
[55,] 4545 4548 1.27175000 0.09571268 1.19303333 1.35046667
[56,] 4549 4863 0.39110159 0.18810262 0.37366880 0.40853437
[57,] 4864 4963 0.47486000 0.26307283 0.43158837 0.51813163
[58,] 4964 4980 1.02270588 0.49126759 0.82672175 1.21869001
[59,] 4981 5146 0.35089759 0.13298570 0.33391992 0.36787526
[60,] 5147 5636 0.41125306 0.23248036 0.39397816 0.42852796
[61,] 5637 5718 0.44039024 0.22202345 0.40006108 0.48071940
[62,] 5719 6011 0.49818430 0.28837213 0.47047365 0.52589495
[63,] 6012 6017 1.02733333 0.18042912 0.90617361 1.14849305
[64,] 6018 6230 0.42732864 0.23357544 0.40100387 0.45365341
[65,] 6231 6235 1.04000000 0.11135529 0.95808695 1.12191305
[66,] 6236 6395 0.30019375 0.11013665 0.28587190 0.31451560
[67,] 6396 6558 0.40439877 0.22889281 0.37490939 0.43388815
[68,] 6559 6565 0.82328571 0.09350528 0.76515383 0.88141760
[69,] 6566 6679 0.27366667 0.05711093 0.26486846 0.28246487
[70,] 6680 6683 0.85625000 0.06570832 0.80220972 0.91029028
[71,] 6684 6803 0.28623333 0.12169632 0.26796015 0.30450651
[72,] 6804 7146 0.52422449 0.29807168 0.49775163 0.55069735
[73,] 7147 7350 0.31261275 0.26878429 0.28165880 0.34356669
[74,] 7351 7671 0.42755452 0.29384902 0.40057719 0.45453185
[75,] 7672 8109 0.40544749 0.32093285 0.38022405 0.43067093
[76,] 8110 8312 0.38780788 0.32644096 0.35012155 0.42549422
[77,] 8313 8516 0.40003922 0.33977798 0.36090944 0.43916899
[78,] 8517 8893 0.37877454 0.28384803 0.35472856 0.40282051
[79,] 8894 9536 0.44813997 0.32546790 0.42702794 0.46925200
[80,] 9537 9765 0.65152402 0.32387550 0.61632037 0.68672766
[81,] 9766 10068 0.49356106 0.25400972 0.46955857 0.51756354
[82,] 10069 10134 0.55030303 0.34243544 0.48097099 0.61963507
[83,] 10135 10140 1.35683333 0.22070017 1.20863125 1.50503542
[84,] 10141 10236 0.36204167 0.16197329 0.33485005 0.38923328
[85,] 10237 10332 0.43502083 0.26903739 0.38985560 0.48018607
[86,] 10333 10437 0.35643810 0.17702993 0.32802103 0.38485517
[87,] 10438 10517 0.37961250 0.18511826 0.34556921 0.41365579
[88,] 10518 10591 0.55533784 0.37381261 0.48386100 0.62681468
[89,] 10592 10928 0.59558457 0.32778050 0.56621515 0.62495399
[90,] 10929 10937 1.56755556 0.25295015 1.42886690 1.70624421
[91,] 10938 11783 0.52851655 0.30350088 0.51135320 0.54567990
[92,] 11784 11969 0.49110753 0.26559050 0.45907558 0.52313947
[93,] 11970 12056 0.39251724 0.17475344 0.36170003 0.42333445
[94,] 12057 12131 0.45756000 0.25458680 0.40920599 0.50591401
[95,] 12132 12726 0.51640672 0.26338135 0.49864628 0.53416717
[96,] 12727 12732 1.29900000 0.20395686 1.16204120 1.43595880
[97,] 12733 13382 0.51832462 0.27554900 0.50054717 0.53610206
[98,] 13383 13603 0.43812670 0.24908223 0.41056704 0.46568636
[99,] 13604 13870 0.43283521 0.20554365 0.41214448 0.45352593
[100,] 13871 14255 0.40451948 0.21354805 0.38661785 0.42242111
[101,] 14256 14551 0.29983784 0.21176357 0.27959214 0.32008353
[102,] 14552 14650 0.32707071 0.28589020 0.27980905 0.37433236
[103,] 14651 14656 0.99883333 0.15830656 0.89252910 1.10513756
[104,] 14657 14964 0.31847727 0.12287769 0.30696065 0.32999389
[105,] 14965 15052 0.38471591 0.16570303 0.35566121 0.41377061
[106,] 15053 15059 1.37171429 0.11153731 1.30237196 1.44105661
[107,] 15060 15128 0.41749275 0.21528795 0.37486206 0.46012345
[108,] 15129 15315 0.39243316 0.20651343 0.36759297 0.41727334
[109,] 15316 15342 0.62296296 0.27143245 0.53704042 0.70888551
[110,] 15343 15376 0.38794118 0.13503713 0.34984854 0.42603381
[111,] 15377 15382 0.31166667 0.08084965 0.25737542 0.36595791
[112,] 15383 15386 0.28250000 0.07889867 0.21761162 0.34738838
[113,] 15387 15389 0.29366667 0.08485478 0.21308376 0.37424957
[114,] 15390 15400 0.27827273 0.04756909 0.25468122 0.30186424
[115,] 15401 15832 0.29526157 0.07287639 0.28949428 0.30102887
[116,] 15833 15838 0.94333333 0.04131182 0.91559209 0.97107458
[117,] 15839 16069 0.31194805 0.08370563 0.30288914 0.32100696
[118,] 16070 16280 0.32904265 0.16637795 0.31020262 0.34788269
[119,] 16281 16307 0.41777778 0.29539365 0.32427026 0.51128530
[120,] 16308 16496 0.30491005 0.06815562 0.29675554 0.31306457
[121,] 16497 16539 0.63139535 0.53210535 0.49792320 0.76486750
[122,] 16540 16602 0.51480952 0.28441444 0.45586973 0.57374932
[123,] 16603 16612 0.36000000 0.01632993 0.35150601 0.36849399
[124,] 16613 16663 0.34460784 0.15144307 0.30972663 0.37948906
[125,] 16664 17012 0.30289112 0.08392813 0.29550150 0.31028074
[126,] 17013 17046 0.60958824 0.29497931 0.52637750 0.69279897
[127,] 17047 17301 0.31431765 0.06981058 0.30712683 0.32150847
[128,] 17302 17309 0.62087500 0.30015779 0.44632017 0.79542983
[129,] 17310 17404 0.29954737 0.07243482 0.28732338 0.31177135
[130,] 17405 17429 0.47876000 0.31626707 0.37471739 0.58280261
[131,] 17430 17650 0.30241176 0.07319617 0.29431299 0.31051054
[132,] 17651 17658 1.01375000 0.02825269 0.99731983 1.03018017
[133,] 17659 17908 0.32274000 0.08432301 0.31396790 0.33151210
[134,] 17909 18014 0.36847170 0.12502687 0.34849712 0.38844628
[135,] 18015 18072 0.45996552 0.22579233 0.41119892 0.50873211
[136,] 18073 18074 1.84500000 0.60104076 1.14593721 2.54406279
[137,] 18075 18173 0.49686869 0.27816552 0.45088403 0.54285334
[138,] 18174 18264 0.32750549 0.08028289 0.31366252 0.34134847
[139,] 18265 18360 0.34258333 0.19949234 0.30909312 0.37607354
[140,] 18361 18433 0.38735616 0.20038306 0.34877931 0.42593302
[141,] 18434 18437 1.30000000 0.06377042 1.24755350 1.35244650
[142,] 18438 18608 0.32561404 0.07849521 0.31574051 0.33548756
[143,] 18609 18615 1.01857143 0.12992672 0.93779648 1.09934638
[144,] 18616 18788 0.30564740 0.07546235 0.29621039 0.31508441
[145,] 18789 18899 0.30182883 0.05804275 0.29276704 0.31089061
[146,] 18900 18905 1.08166667 0.04070217 1.05433481 1.10899853
[147,] 18906 19048 0.34625874 0.09462407 0.33324324 0.35927424
[148,] 19049 19050 1.60950000 0.71771338 0.77473678 2.44426322
[149,] 19051 19261 0.32608531 0.10333138 0.31438444 0.33778618
[150,] 19262 19267 0.92983333 0.05562523 0.89248051 0.96718616
[151,] 19268 19519 0.30967063 0.07631779 0.30176289 0.31757838
[152,] 19520 19624 0.29762857 0.08261440 0.28436720 0.31088994
[153,] 19625 19629 0.96340000 0.11106440 0.88170093 1.04509907
[154,] 19630 19845 0.29996296 0.08392853 0.29056984 0.30935609
[155,] 19846 19852 0.88614286 0.10043311 0.82370397 0.94858174
[156,] 19853 20015 0.28745399 0.07412420 0.27790420 0.29700377
[157,] 20016 20017 1.65850000 0.26657926 1.34844509 1.96855491
[158,] 20018 20092 0.42212000 0.17762259 0.38838390 0.45585610
[159,] 20093 20103 0.87218182 0.47699556 0.63561967 1.10874396
[160,] 20104 20296 0.30757513 0.08093623 0.29799235 0.31715791
[161,] 20297 20340 0.60940909 0.39307880 0.51193694 0.70688124
[162,] 20341 20478 0.36581159 0.20908988 0.33653499 0.39508820
[163,] 20479 20569 0.44713187 0.15490586 0.42042184 0.47384190
[164,] 20570 20578 2.04088889 0.63784823 1.69116657 2.39061121
[165,] 20579 20720 0.57975352 0.26651694 0.54296538 0.61654167
[166,] 20721 20726 1.20166667 0.18071156 1.08031729 1.32301605
[ reached getOption("max.print") -- omitted 788 rows ]
plot(summary(bcp))
[1] "Change points"
[1] "Segments"
begin end mean SD LL of CI UL of CI
[1,] 1 68 0.11851471 0.05251481 0.10803969 0.12898972
[2,] 69 89 0.70085714 0.55165300 0.50284875 0.89886553
[3,] 90 233 0.14222917 0.09243684 0.12955874 0.15489959
[4,] 234 240 0.91714286 0.55476224 0.57224926 1.26203645
[5,] 241 408 0.37915476 0.27470555 0.34429375 0.41401578
[6,] 409 410 1.73000000 0.66468037 0.95691880 2.50308120
[7,] 411 524 0.45407895 0.32414913 0.40414226 0.50401563
[8,] 525 526 1.84250000 0.59609102 1.14919420 2.53580580
[9,] 527 599 0.17747945 0.06771880 0.16444253 0.19051638
[10,] 600 602 1.19966667 0.60550007 0.62464948 1.77468385
[11,] 603 696 0.22370213 0.15978499 0.19659401 0.25081024
[12,] 697 724 0.26989286 0.26216920 0.18839805 0.35138766
[13,] 725 792 0.21539706 0.23737175 0.16804904 0.26274508
[14,] 793 843 0.18211765 0.20866132 0.13405761 0.23017769
[15,] 844 888 0.14957778 0.17634812 0.10633717 0.19281839
[16,] 889 938 0.19734000 0.19177406 0.15273001 0.24194999
[17,] 939 943 0.86680000 0.05580502 0.82574978 0.90785022
[18,] 944 1018 0.17470667 0.10118618 0.15548824 0.19392509
[19,] 1019 1032 0.58428571 0.34496364 0.43263776 0.73593367
[20,] 1033 1142 0.18816364 0.08463429 0.17489039 0.20143689
[21,] 1143 1176 0.18558824 0.15582024 0.14163289 0.22954358
[22,] 1177 1178 1.59500000 0.60952605 0.88606809 2.30393191
[23,] 1179 1368 0.32744737 0.28211650 0.29378234 0.36111239
[24,] 1369 1375 0.95057143 0.51726327 0.62899080 1.27215205
[25,] 1376 1466 0.21749451 0.25720279 0.17314567 0.26184334
[26,] 1467 1560 0.21709574 0.22015376 0.17974584 0.25444564
[27,] 1561 1605 0.30462222 0.40168567 0.20612878 0.40311567
[28,] 1606 1714 0.17048624 0.14168952 0.14816328 0.19280920
[29,] 1715 1955 0.14351452 0.11244080 0.13160093 0.15542811
[30,] 1956 1959 0.84625000 0.16573950 0.70994139 0.98255861
[31,] 1960 2067 0.18087037 0.09166977 0.16636123 0.19537951
[32,] 2068 2144 0.19009091 0.11276725 0.16895286 0.21122896
[33,] 2145 2147 1.92800000 0.91683314 1.05732328 2.79867672
[34,] 2148 2232 0.44197647 0.43827136 0.36378473 0.52016821
[35,] 2233 2252 0.79275000 0.43621348 0.63231049 0.95318951
[36,] 2253 2328 0.15798684 0.11320336 0.13662790 0.17934579
[37,] 2329 2335 0.90457143 0.42456050 0.64062376 1.16851909
[38,] 2336 2472 0.20097810 0.18653806 0.17476403 0.22719217
[39,] 2473 2659 0.17288235 0.10114196 0.16071663 0.18504807
[40,] 2660 2670 0.43463636 0.27245266 0.29951564 0.56975709
[41,] 2671 2672 0.33850000 0.18031223 0.12878116 0.54821884
[42,] 2673 2676 0.37275000 0.11853375 0.27526467 0.47023533
[43,] 2677 3132 0.27140132 0.12122497 0.26206368 0.28073895
[44,] 3133 3414 0.24971277 0.10382509 0.23954314 0.25988239
[45,] 3415 3485 0.26271831 0.08814455 0.24551177 0.27992485
[46,] 3486 3548 0.78198413 0.43995383 0.69081158 0.87315667
[47,] 3549 3634 0.51816279 0.27488166 0.46940727 0.56691831
[48,] 3635 3718 0.33130952 0.13270603 0.30749301 0.35512604
[49,] 3719 3721 0.17733333 0.04707795 0.13262544 0.22204122
[50,] 3722 4070 0.15686819 0.10889972 0.14727990 0.16645649
[51,] 4071 4077 0.79457143 0.04656485 0.76562223 0.82352062
[52,] 4078 4292 0.15275349 0.10925063 0.14049796 0.16500902
[53,] 4293 4445 0.38272549 0.31320973 0.34107532 0.42437566
[54,] 4446 4544 0.58247475 0.29179862 0.53423635 0.63071315
[55,] 4545 4548 1.27175000 0.09571268 1.19303333 1.35046667
[56,] 4549 4863 0.39110159 0.18810262 0.37366880 0.40853437
[57,] 4864 4963 0.47486000 0.26307283 0.43158837 0.51813163
[58,] 4964 4980 1.02270588 0.49126759 0.82672175 1.21869001
[59,] 4981 5146 0.35089759 0.13298570 0.33391992 0.36787526
[60,] 5147 5636 0.41125306 0.23248036 0.39397816 0.42852796
[61,] 5637 5718 0.44039024 0.22202345 0.40006108 0.48071940
[62,] 5719 6011 0.49818430 0.28837213 0.47047365 0.52589495
[63,] 6012 6017 1.02733333 0.18042912 0.90617361 1.14849305
[64,] 6018 6230 0.42732864 0.23357544 0.40100387 0.45365341
[65,] 6231 6235 1.04000000 0.11135529 0.95808695 1.12191305
[66,] 6236 6395 0.30019375 0.11013665 0.28587190 0.31451560
[67,] 6396 6558 0.40439877 0.22889281 0.37490939 0.43388815
[68,] 6559 6565 0.82328571 0.09350528 0.76515383 0.88141760
[69,] 6566 6679 0.27366667 0.05711093 0.26486846 0.28246487
[70,] 6680 6683 0.85625000 0.06570832 0.80220972 0.91029028
[71,] 6684 6803 0.28623333 0.12169632 0.26796015 0.30450651
[72,] 6804 7146 0.52422449 0.29807168 0.49775163 0.55069735
[73,] 7147 7350 0.31261275 0.26878429 0.28165880 0.34356669
[74,] 7351 7671 0.42755452 0.29384902 0.40057719 0.45453185
[75,] 7672 8109 0.40544749 0.32093285 0.38022405 0.43067093
[76,] 8110 8312 0.38780788 0.32644096 0.35012155 0.42549422
[77,] 8313 8516 0.40003922 0.33977798 0.36090944 0.43916899
[78,] 8517 8893 0.37877454 0.28384803 0.35472856 0.40282051
[79,] 8894 9536 0.44813997 0.32546790 0.42702794 0.46925200
[80,] 9537 9765 0.65152402 0.32387550 0.61632037 0.68672766
[81,] 9766 10068 0.49356106 0.25400972 0.46955857 0.51756354
[82,] 10069 10134 0.55030303 0.34243544 0.48097099 0.61963507
[83,] 10135 10140 1.35683333 0.22070017 1.20863125 1.50503542
[84,] 10141 10236 0.36204167 0.16197329 0.33485005 0.38923328
[85,] 10237 10332 0.43502083 0.26903739 0.38985560 0.48018607
[86,] 10333 10437 0.35643810 0.17702993 0.32802103 0.38485517
[87,] 10438 10517 0.37961250 0.18511826 0.34556921 0.41365579
[88,] 10518 10591 0.55533784 0.37381261 0.48386100 0.62681468
[89,] 10592 10928 0.59558457 0.32778050 0.56621515 0.62495399
[90,] 10929 10937 1.56755556 0.25295015 1.42886690 1.70624421
[91,] 10938 11783 0.52851655 0.30350088 0.51135320 0.54567990
[92,] 11784 11969 0.49110753 0.26559050 0.45907558 0.52313947
[93,] 11970 12056 0.39251724 0.17475344 0.36170003 0.42333445
[94,] 12057 12131 0.45756000 0.25458680 0.40920599 0.50591401
[95,] 12132 12726 0.51640672 0.26338135 0.49864628 0.53416717
[96,] 12727 12732 1.29900000 0.20395686 1.16204120 1.43595880
[97,] 12733 13382 0.51832462 0.27554900 0.50054717 0.53610206
[98,] 13383 13603 0.43812670 0.24908223 0.41056704 0.46568636
[99,] 13604 13870 0.43283521 0.20554365 0.41214448 0.45352593
[100,] 13871 14255 0.40451948 0.21354805 0.38661785 0.42242111
[101,] 14256 14551 0.29983784 0.21176357 0.27959214 0.32008353
[102,] 14552 14650 0.32707071 0.28589020 0.27980905 0.37433236
[103,] 14651 14656 0.99883333 0.15830656 0.89252910 1.10513756
[104,] 14657 14964 0.31847727 0.12287769 0.30696065 0.32999389
[105,] 14965 15052 0.38471591 0.16570303 0.35566121 0.41377061
[106,] 15053 15059 1.37171429 0.11153731 1.30237196 1.44105661
[107,] 15060 15128 0.41749275 0.21528795 0.37486206 0.46012345
[108,] 15129 15315 0.39243316 0.20651343 0.36759297 0.41727334
[109,] 15316 15342 0.62296296 0.27143245 0.53704042 0.70888551
[110,] 15343 15376 0.38794118 0.13503713 0.34984854 0.42603381
[111,] 15377 15382 0.31166667 0.08084965 0.25737542 0.36595791
[112,] 15383 15386 0.28250000 0.07889867 0.21761162 0.34738838
[113,] 15387 15389 0.29366667 0.08485478 0.21308376 0.37424957
[114,] 15390 15400 0.27827273 0.04756909 0.25468122 0.30186424
[115,] 15401 15832 0.29526157 0.07287639 0.28949428 0.30102887
[116,] 15833 15838 0.94333333 0.04131182 0.91559209 0.97107458
[117,] 15839 16069 0.31194805 0.08370563 0.30288914 0.32100696
[118,] 16070 16280 0.32904265 0.16637795 0.31020262 0.34788269
[119,] 16281 16307 0.41777778 0.29539365 0.32427026 0.51128530
[120,] 16308 16496 0.30491005 0.06815562 0.29675554 0.31306457
[121,] 16497 16539 0.63139535 0.53210535 0.49792320 0.76486750
[122,] 16540 16602 0.51480952 0.28441444 0.45586973 0.57374932
[123,] 16603 16612 0.36000000 0.01632993 0.35150601 0.36849399
[124,] 16613 16663 0.34460784 0.15144307 0.30972663 0.37948906
[125,] 16664 17012 0.30289112 0.08392813 0.29550150 0.31028074
[126,] 17013 17046 0.60958824 0.29497931 0.52637750 0.69279897
[127,] 17047 17301 0.31431765 0.06981058 0.30712683 0.32150847
[128,] 17302 17309 0.62087500 0.30015779 0.44632017 0.79542983
[129,] 17310 17404 0.29954737 0.07243482 0.28732338 0.31177135
[130,] 17405 17429 0.47876000 0.31626707 0.37471739 0.58280261
[131,] 17430 17650 0.30241176 0.07319617 0.29431299 0.31051054
[132,] 17651 17658 1.01375000 0.02825269 0.99731983 1.03018017
[133,] 17659 17908 0.32274000 0.08432301 0.31396790 0.33151210
[134,] 17909 18014 0.36847170 0.12502687 0.34849712 0.38844628
[135,] 18015 18072 0.45996552 0.22579233 0.41119892 0.50873211
[136,] 18073 18074 1.84500000 0.60104076 1.14593721 2.54406279
[137,] 18075 18173 0.49686869 0.27816552 0.45088403 0.54285334
[138,] 18174 18264 0.32750549 0.08028289 0.31366252 0.34134847
[139,] 18265 18360 0.34258333 0.19949234 0.30909312 0.37607354
[140,] 18361 18433 0.38735616 0.20038306 0.34877931 0.42593302
[141,] 18434 18437 1.30000000 0.06377042 1.24755350 1.35244650
[142,] 18438 18608 0.32561404 0.07849521 0.31574051 0.33548756
[143,] 18609 18615 1.01857143 0.12992672 0.93779648 1.09934638
[144,] 18616 18788 0.30564740 0.07546235 0.29621039 0.31508441
[145,] 18789 18899 0.30182883 0.05804275 0.29276704 0.31089061
[146,] 18900 18905 1.08166667 0.04070217 1.05433481 1.10899853
[147,] 18906 19048 0.34625874 0.09462407 0.33324324 0.35927424
[148,] 19049 19050 1.60950000 0.71771338 0.77473678 2.44426322
[149,] 19051 19261 0.32608531 0.10333138 0.31438444 0.33778618
[150,] 19262 19267 0.92983333 0.05562523 0.89248051 0.96718616
[151,] 19268 19519 0.30967063 0.07631779 0.30176289 0.31757838
[152,] 19520 19624 0.29762857 0.08261440 0.28436720 0.31088994
[153,] 19625 19629 0.96340000 0.11106440 0.88170093 1.04509907
[154,] 19630 19845 0.29996296 0.08392853 0.29056984 0.30935609
[155,] 19846 19852 0.88614286 0.10043311 0.82370397 0.94858174
[156,] 19853 20015 0.28745399 0.07412420 0.27790420 0.29700377
[157,] 20016 20017 1.65850000 0.26657926 1.34844509 1.96855491
[158,] 20018 20092 0.42212000 0.17762259 0.38838390 0.45585610
[159,] 20093 20103 0.87218182 0.47699556 0.63561967 1.10874396
[160,] 20104 20296 0.30757513 0.08093623 0.29799235 0.31715791
[161,] 20297 20340 0.60940909 0.39307880 0.51193694 0.70688124
[162,] 20341 20478 0.36581159 0.20908988 0.33653499 0.39508820
[163,] 20479 20569 0.44713187 0.15490586 0.42042184 0.47384190
[164,] 20570 20578 2.04088889 0.63784823 1.69116657 2.39061121
[165,] 20579 20720 0.57975352 0.26651694 0.54296538 0.61654167
[166,] 20721 20726 1.20166667 0.18071156 1.08031729 1.32301605
[ reached getOption("max.print") -- omitted 788 rows ]
Nice! Looks like it still works over a longer period. (An advantage of an onlineBcp model?)
It looks like any interval with a mean above about 2.0 kWh should qualify as an EV charging session. - Do we need to do anything to avoid mixups with high HVAC usage? - Do we need to put an ideal length of charge on the intervals? Maximum variation during the period? - Use post.prob to filter out any intervals?
result <- summary(bcp, norm.test = TRUE)
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Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov testWarning: ties should not be present for the Kolmogorov-Smirnov test
[1] "Change points"
[1] "Segments"
begin end mean SD LL of CI UL of CI normality p-value
[1,] 1 68 0.11851471 0.05251481 0.10803969 0.12898972 5.558635e-02
[2,] 69 89 0.70085714 0.55165300 0.50284875 0.89886553 2.356642e-01
[3,] 90 233 0.14222917 0.09243684 0.12955874 0.15489959 5.017888e-02
[4,] 234 240 0.91714286 0.55476224 0.57224926 1.26203645 8.598808e-01
[5,] 241 408 0.37915476 0.27470555 0.34429375 0.41401578 2.983556e-05
[6,] 409 410 1.73000000 0.66468037 0.95691880 2.50308120 9.991595e-01
[7,] 411 524 0.45407895 0.32414913 0.40414226 0.50401563 1.754392e-03
[8,] 525 526 1.84250000 0.59609102 1.14919420 2.53580580 9.991595e-01
[9,] 527 599 0.17747945 0.06771880 0.16444253 0.19051638 2.609959e-02
[10,] 600 602 1.19966667 0.60550007 0.62464948 1.77468385 9.460188e-01
[11,] 603 696 0.22370213 0.15978499 0.19659401 0.25081024 2.906650e-04
[12,] 697 724 0.26989286 0.26216920 0.18839805 0.35138766 1.914599e-02
[13,] 725 792 0.21539706 0.23737175 0.16804904 0.26274508 1.569400e-04
[14,] 793 843 0.18211765 0.20866132 0.13405761 0.23017769 3.147528e-03
[15,] 844 888 0.14957778 0.17634812 0.10633717 0.19281839 4.439415e-04
[16,] 889 938 0.19734000 0.19177406 0.15273001 0.24194999 5.771098e-05
[17,] 939 943 0.86680000 0.05580502 0.82574978 0.90785022 5.927433e-01
[18,] 944 1018 0.17470667 0.10118618 0.15548824 0.19392509 1.033167e-01
[19,] 1019 1032 0.58428571 0.34496364 0.43263776 0.73593367 2.792816e-01
[20,] 1033 1142 0.18816364 0.08463429 0.17489039 0.20143689 4.438916e-03
[21,] 1143 1176 0.18558824 0.15582024 0.14163289 0.22954358 1.927116e-02
[22,] 1177 1178 1.59500000 0.60952605 0.88606809 2.30393191 9.991595e-01
[23,] 1179 1368 0.32744737 0.28211650 0.29378234 0.36111239 1.199396e-07
[24,] 1369 1375 0.95057143 0.51726327 0.62899080 1.27215205 3.258514e-01
[25,] 1376 1466 0.21749451 0.25720279 0.17314567 0.26184334 3.567013e-11
[26,] 1467 1560 0.21709574 0.22015376 0.17974584 0.25444564 6.201917e-06
[27,] 1561 1605 0.30462222 0.40168567 0.20612878 0.40311567 1.684396e-03
[28,] 1606 1714 0.17048624 0.14168952 0.14816328 0.19280920 6.067085e-04
[29,] 1715 1955 0.14351452 0.11244080 0.13160093 0.15542811 1.000881e-06
[30,] 1956 1959 0.84625000 0.16573950 0.70994139 0.98255861 7.932280e-01
[31,] 1960 2067 0.18087037 0.09166977 0.16636123 0.19537951 1.412459e-01
[32,] 2068 2144 0.19009091 0.11276725 0.16895286 0.21122896 1.848765e-01
[33,] 2145 2147 1.92800000 0.91683314 1.05732328 2.79867672 7.289916e-01
[34,] 2148 2232 0.44197647 0.43827136 0.36378473 0.52016821 1.045416e-03
[35,] 2233 2252 0.79275000 0.43621348 0.63231049 0.95318951 9.509594e-01
[36,] 2253 2328 0.15798684 0.11320336 0.13662790 0.17934579 3.341529e-03
[37,] 2329 2335 0.90457143 0.42456050 0.64062376 1.16851909 7.828116e-01
[38,] 2336 2472 0.20097810 0.18653806 0.17476403 0.22719217 3.578956e-08
[39,] 2473 2659 0.17288235 0.10114196 0.16071663 0.18504807 4.748881e-04
[40,] 2660 2670 0.43463636 0.27245266 0.29951564 0.56975709 9.083194e-01
[41,] 2671 2672 0.33850000 0.18031223 0.12878116 0.54821884 9.991595e-01
[42,] 2673 2676 0.37275000 0.11853375 0.27526467 0.47023533 9.750718e-01
[43,] 2677 3132 0.27140132 0.12122497 0.26206368 0.28073895 5.356035e-09
[44,] 3133 3414 0.24971277 0.10382509 0.23954314 0.25988239 8.637535e-14
[45,] 3415 3485 0.26271831 0.08814455 0.24551177 0.27992485 9.550424e-07
[46,] 3486 3548 0.78198413 0.43995383 0.69081158 0.87315667 1.218112e-01
[47,] 3549 3634 0.51816279 0.27488166 0.46940727 0.56691831 2.263517e-02
[48,] 3635 3718 0.33130952 0.13270603 0.30749301 0.35512604 1.468338e-02
[49,] 3719 3721 0.17733333 0.04707795 0.13262544 0.22204122 8.058098e-01
[50,] 3722 4070 0.15686819 0.10889972 0.14727990 0.16645649 1.034916e-05
[51,] 4071 4077 0.79457143 0.04656485 0.76562223 0.82352062 9.627227e-01
[52,] 4078 4292 0.15275349 0.10925063 0.14049796 0.16500902 1.564990e-02
[53,] 4293 4445 0.38272549 0.31320973 0.34107532 0.42437566 8.694972e-03
[54,] 4446 4544 0.58247475 0.29179862 0.53423635 0.63071315 1.060064e-02
[55,] 4545 4548 1.27175000 0.09571268 1.19303333 1.35046667 8.235734e-01
[56,] 4549 4863 0.39110159 0.18810262 0.37366880 0.40853437 1.393781e-05
[57,] 4864 4963 0.47486000 0.26307283 0.43158837 0.51813163 6.418883e-04
[58,] 4964 4980 1.02270588 0.49126759 0.82672175 1.21869001 9.594757e-01
[59,] 4981 5146 0.35089759 0.13298570 0.33391992 0.36787526 4.708571e-05
[60,] 5147 5636 0.41125306 0.23248036 0.39397816 0.42852796 1.728617e-13
[61,] 5637 5718 0.44039024 0.22202345 0.40006108 0.48071940 1.462427e-02
[62,] 5719 6011 0.49818430 0.28837213 0.47047365 0.52589495 4.459741e-04
[63,] 6012 6017 1.02733333 0.18042912 0.90617361 1.14849305 6.175601e-01
[64,] 6018 6230 0.42732864 0.23357544 0.40100387 0.45365341 6.726460e-03
[65,] 6231 6235 1.04000000 0.11135529 0.95808695 1.12191305 9.340271e-01
[66,] 6236 6395 0.30019375 0.11013665 0.28587190 0.31451560 7.056873e-06
[67,] 6396 6558 0.40439877 0.22889281 0.37490939 0.43388815 2.036173e-03
[68,] 6559 6565 0.82328571 0.09350528 0.76515383 0.88141760 3.660591e-01
[69,] 6566 6679 0.27366667 0.05711093 0.26486846 0.28246487 6.685227e-02
[70,] 6680 6683 0.85625000 0.06570832 0.80220972 0.91029028 7.615567e-01
[71,] 6684 6803 0.28623333 0.12169632 0.26796015 0.30450651 1.462045e-03
[72,] 6804 7146 0.52422449 0.29807168 0.49775163 0.55069735 1.414514e-05
[73,] 7147 7350 0.31261275 0.26878429 0.28165880 0.34356669 1.471900e-05
[74,] 7351 7671 0.42755452 0.29384902 0.40057719 0.45453185 4.247697e-02
[75,] 7672 8109 0.40544749 0.32093285 0.38022405 0.43067093 1.267526e-04
[76,] 8110 8312 0.38780788 0.32644096 0.35012155 0.42549422 4.003263e-03
[77,] 8313 8516 0.40003922 0.33977798 0.36090944 0.43916899 3.147864e-03
[78,] 8517 8893 0.37877454 0.28384803 0.35472856 0.40282051 9.653318e-04
[79,] 8894 9536 0.44813997 0.32546790 0.42702794 0.46925200 5.326316e-05
[80,] 9537 9765 0.65152402 0.32387550 0.61632037 0.68672766 7.852331e-03
[81,] 9766 10068 0.49356106 0.25400972 0.46955857 0.51756354 1.900428e-02
[82,] 10069 10134 0.55030303 0.34243544 0.48097099 0.61963507 6.081360e-02
[83,] 10135 10140 1.35683333 0.22070017 1.20863125 1.50503542 4.721032e-01
[84,] 10141 10236 0.36204167 0.16197329 0.33485005 0.38923328 4.720172e-03
[85,] 10237 10332 0.43502083 0.26903739 0.38985560 0.48018607 3.120705e-02
[86,] 10333 10437 0.35643810 0.17702993 0.32802103 0.38485517 1.430184e-02
[87,] 10438 10517 0.37961250 0.18511826 0.34556921 0.41365579 7.363339e-02
[88,] 10518 10591 0.55533784 0.37381261 0.48386100 0.62681468 4.075243e-02
[89,] 10592 10928 0.59558457 0.32778050 0.56621515 0.62495399 6.183850e-03
[90,] 10929 10937 1.56755556 0.25295015 1.42886690 1.70624421 8.302274e-01
[91,] 10938 11783 0.52851655 0.30350088 0.51135320 0.54567990 5.097511e-11
[92,] 11784 11969 0.49110753 0.26559050 0.45907558 0.52313947 9.979034e-03
[93,] 11970 12056 0.39251724 0.17475344 0.36170003 0.42333445 7.095404e-02
[94,] 12057 12131 0.45756000 0.25458680 0.40920599 0.50591401 1.908937e-02
[95,] 12132 12726 0.51640672 0.26338135 0.49864628 0.53416717 2.628987e-09
[96,] 12727 12732 1.29900000 0.20395686 1.16204120 1.43595880 9.826903e-01
[97,] 12733 13382 0.51832462 0.27554900 0.50054717 0.53610206 5.502915e-10
[98,] 13383 13603 0.43812670 0.24908223 0.41056704 0.46568636 3.198705e-07
[99,] 13604 13870 0.43283521 0.20554365 0.41214448 0.45352593 9.398370e-06
[100,] 13871 14255 0.40451948 0.21354805 0.38661785 0.42242111 4.476719e-11
[101,] 14256 14551 0.29983784 0.21176357 0.27959214 0.32008353 2.201160e-10
[102,] 14552 14650 0.32707071 0.28589020 0.27980905 0.37433236 8.394495e-05
[103,] 14651 14656 0.99883333 0.15830656 0.89252910 1.10513756 8.826387e-01
[104,] 14657 14964 0.31847727 0.12287769 0.30696065 0.32999389 2.299932e-02
[105,] 14965 15052 0.38471591 0.16570303 0.35566121 0.41377061 2.742575e-05
[106,] 15053 15059 1.37171429 0.11153731 1.30237196 1.44105661 8.768646e-01
[107,] 15060 15128 0.41749275 0.21528795 0.37486206 0.46012345 5.601728e-03
[108,] 15129 15315 0.39243316 0.20651343 0.36759297 0.41727334 6.875991e-06
[109,] 15316 15342 0.62296296 0.27143245 0.53704042 0.70888551 6.122554e-01
[110,] 15343 15376 0.38794118 0.13503713 0.34984854 0.42603381 5.095012e-01
[111,] 15377 15382 0.31166667 0.08084965 0.25737542 0.36595791 6.752876e-01
[112,] 15383 15386 0.28250000 0.07889867 0.21761162 0.34738838 5.126036e-01
[113,] 15387 15389 0.29366667 0.08485478 0.21308376 0.37424957 8.384185e-01
[114,] 15390 15400 0.27827273 0.04756909 0.25468122 0.30186424 2.677207e-01
[115,] 15401 15832 0.29526157 0.07287639 0.28949428 0.30102887 2.749247e-05
[116,] 15833 15838 0.94333333 0.04131182 0.91559209 0.97107458 9.928218e-01
[117,] 15839 16069 0.31194805 0.08370563 0.30288914 0.32100696 1.579487e-05
[118,] 16070 16280 0.32904265 0.16637795 0.31020262 0.34788269 2.123990e-11
[119,] 16281 16307 0.41777778 0.29539365 0.32427026 0.51128530 1.956821e-03
[120,] 16308 16496 0.30491005 0.06815562 0.29675554 0.31306457 3.143062e-02
[121,] 16497 16539 0.63139535 0.53210535 0.49792320 0.76486750 1.454945e-04
[122,] 16540 16602 0.51480952 0.28441444 0.45586973 0.57374932 1.797704e-03
[123,] 16603 16612 0.36000000 0.01632993 0.35150601 0.36849399 8.186212e-01
[124,] 16613 16663 0.34460784 0.15144307 0.30972663 0.37948906 1.685084e-03
[125,] 16664 17012 0.30289112 0.08392813 0.29550150 0.31028074 1.424762e-06
[126,] 17013 17046 0.60958824 0.29497931 0.52637750 0.69279897 2.099508e-01
[127,] 17047 17301 0.31431765 0.06981058 0.30712683 0.32150847 9.057676e-03
[128,] 17302 17309 0.62087500 0.30015779 0.44632017 0.79542983 8.517411e-01
[129,] 17310 17404 0.29954737 0.07243482 0.28732338 0.31177135 1.722243e-04
[130,] 17405 17429 0.47876000 0.31626707 0.37471739 0.58280261 3.673417e-03
[131,] 17430 17650 0.30241176 0.07319617 0.29431299 0.31051054 1.689783e-05
[132,] 17651 17658 1.01375000 0.02825269 0.99731983 1.03018017 9.393540e-01
[133,] 17659 17908 0.32274000 0.08432301 0.31396790 0.33151210 1.444155e-05
[134,] 17909 18014 0.36847170 0.12502687 0.34849712 0.38844628 3.577015e-02
[135,] 18015 18072 0.45996552 0.22579233 0.41119892 0.50873211 2.914935e-02
[136,] 18073 18074 1.84500000 0.60104076 1.14593721 2.54406279 9.991595e-01
[137,] 18075 18173 0.49686869 0.27816552 0.45088403 0.54285334 3.669993e-04
[138,] 18174 18264 0.32750549 0.08028289 0.31366252 0.34134847 2.043566e-02
[139,] 18265 18360 0.34258333 0.19949234 0.30909312 0.37607354 1.058334e-05
[140,] 18361 18433 0.38735616 0.20038306 0.34877931 0.42593302 2.253355e-04
[141,] 18434 18437 1.30000000 0.06377042 1.24755350 1.35244650 7.318359e-01
[142,] 18438 18608 0.32561404 0.07849521 0.31574051 0.33548756 2.529906e-03
[ reached getOption("max.print") -- omitted 812 rows ]
bcp_segments <- as_tibble(result$result$segment) %>%
mutate(length = end - begin) %>%
mutate(lag_mean = lag(mean,1)) %>%
mutate(lead_mean = lead(mean,1)) %>%
mutate(adj = (lag_mean+lead_mean)/2)
bcp_segments %>%
filter(mean > 2.0)
NA
sample_adj <- sample %>%
left_join(bcp_segments, by = join_by(time_x>=begin,time_x<=end)) %>%
mutate(consumed_kWh_adj = case_when(
mean > 2.0 & mean < 3.0 ~ consumed_kWh - (mean - adj),
.default = consumed_kWh
)
)
sample_adj
NA
NA
Re-run on all three years, count charging sessions by month/year